The goals of the Data Analysis Core (DAC) are to promote and facilitate the research undertaken by the Junior Investigators in the COBRE, and to provide support for the development of analytical capabilities at all levels of the COBRE, including technicians and mentors, but especially the Junior Investigators. As potential collaborators for all research projects, the DAC will be an integral part of the research team in study development, as well as providing ongoing statistical and analytical advice for each individual project. Centralized analysis through the DAC will enable standardization of statistical diagnostic checks of distributional assumptions, outliers (overly influential data points), colinearity, etc., ensuring that all analyses are of the same high quality and rigor. The foundation of the DAC provides more efficient management of the data shared between projects, better quality control of both data and analysis, and easier assessment of overall progress of the COBRE through standardization of data and analysis presentation. Certainly this will provide an easier and more coherent assessment of the progress of the COBRE for the Administrative Core, for the Projects, and for the Advisory Committee. Even more exciting is the possibility that from the overall picture provided, previously unrealized relationships between different facets in the etiology of SLE will become apparent. The specific functions of the DAC will include providing experimental design advice and training to Junior Investigators (and to OMRF); assisting, advising, and implementing high quality data management within laboratories; providing state of the art analysis capabilities for data from DNA arrays, genetic linkage, and genetic association studies; and providing high level informatics and programming capabilities to Junior Investigators. Services offered by the DAC include consultation on experimental design including power analysis; genetics data analysis including linkage and association approaches, which further subdivide to include fine mapping methods, family-based association and haplotype-based methods, case-control candidate gene methods and multi-locus methods; gene expression analysis from microarray experiments including normalization of microarray data, analysis of differentially expressed genes, analysis of alterations in gene functional association using various clustering methods, identifying genes associated with biochemical pathways of interest and creation of transcription based gene networks, and selection of genes with discriminatory capabilities for disease in genetic data analysis. High quality data analysis of the type provided by the DAC is essential for meaningful interpretation of results from all four Junior Investigator projects.